Synergistic effect of polymorphism, substrate conductivity and electric field stimulation towards enhancing muscle cell growth in vitro

Poly(vinylidene difluoride), a well-known candidate for artificial muscle patch applications is a semi-crystalline polymer with a host of attributes such as piezo- and pyroelectricity, polymorphism along with low dielectric constant and stiffness. The present work explores the unique interplay among the factors (conductivity, polymorphism and electrical stimulation) towards cell proliferation on poly(vinylidene difluoride) (PVDF)-based composites. In this regard, multi-walled carbon nanotubes (MWNTs) are introduced in the PVDF matrix (limited to 2%) through melt mixing to increase the conductivity of PVDF. The addition of MWNTs also led to an increase in the fraction of piezoelectric beta-phase, tensile strength and modulus. The melting and crystallization behaviour of PVDF-MWNT together with FT-IR confirms that the crystallization is found to be aided by the presence of MWNT. The conducting PVDF-MWNTs are used as substrates for the growth of C2C12 mouse myoblast cells and electrical stimulation with a range of field strengths (0-2 V cm(-1)) is intermittently delivered to the cells in culture. The cell viability results suggest that metabolically active cell numbers can statistically increase with electric stimulation up to 1 V cm(-1), only on the PVDF + 2% MWNT. Summarising, the current study highlights the importance of biophysical cues on cellular function at the cell-substrate interface. This study further opens up new avenues in designing conducting substrates, that can be utilized for enhancing cell viability and proliferation and also reconfirms the lack of toxicity of MWNTs, when added in a tailored manner.

Unsupervised training for mandarin broadcast news and conversation transcription

A significant cost in obtaining acoustic training data is the generation of accurate transcriptions. For some sources close-caption data is available. This allows the use of lightly-supervised training techniques. However, for some sources and languages close-caption is not available. In these cases unsupervised training techniques must be used. This paper examines the use of unsupervised techniques for discriminative training. In unsupervised training automatic transcriptions from a recognition system are used for training. As these transcriptions may be errorful data selection may be useful. Two forms of selection are described, one to remove non-target language shows, the other to remove segments with low confidence. Experiments were carried out on a Mandarin transcriptions task. Two types of test data were considered, Broadcast News (BN) and Broadcast Conversations (BC). Results show that the gains from unsupervised discriminative training are highly dependent on the accuracy of the automatic transcriptions. © 2007 IEEE.